Zobrazeno 1 - 10
of 14 965
pro vyhledávání: '"JianXiong, AN"'
Autor:
Zhang, Jinyu, Gu, Yongchong, Gao, Jianxiong, Lin, Haitao, Sun, Qiang, Sun, Xinwei, Xue, Xiangyang, Fu, Yanwei
This paper addresses the challenge of perceiving complete object shapes through visual perception. While prior studies have demonstrated encouraging outcomes in segmenting the visible parts of objects within a scene, amodal segmentation, in particula
Externí odkaz:
http://arxiv.org/abs/2408.03238
Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many
Externí odkaz:
http://arxiv.org/abs/2406.13404
Amodal object completion is a complex task that involves predicting the invisible parts of an object based on visible segments and background information. Learning shape priors is crucial for effective amodal completion, but traditional methods often
Externí odkaz:
http://arxiv.org/abs/2405.19949
Autor:
Zheng, Jinliang, Li, Jianxiong, Cheng, Sijie, Zheng, Yinan, Li, Jiaming, Liu, Jihao, Liu, Yu, Liu, Jingjing, Zhan, Xianyuan
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targeted local region of an image. To achieve more accurate and nuanced mu
Externí odkaz:
http://arxiv.org/abs/2405.19783
Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically miti
Externí odkaz:
http://arxiv.org/abs/2405.08582
Autor:
Li, Jianxiong, Zheng, Jinliang, Zheng, Yinan, Mao, Liyuan, Hu, Xiao, Cheng, Sijie, Niu, Haoyi, Liu, Jihao, Liu, Yu, Liu, Jingjing, Zhang, Ya-Qin, Zhan, Xianyuan
Multimodal pretraining is an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progressions; 2) enforcing temporal consistency of visual representation; 3) capturing
Externí odkaz:
http://arxiv.org/abs/2402.18137
Quantum networks are interconnected by nodes, between singlets which are formed to ensure the successful transmission of information with a probability of 1. However, in real quantum networks, nodes often share a partially entangled state instead of
Externí odkaz:
http://arxiv.org/abs/2401.11862
Autor:
Zheng, Yinan, Li, Jianxiong, Yu, Dongjie, Yang, Yujie, Li, Shengbo Eben, Zhan, Xianyuan, Liu, Jingjing
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety violations in expectation below thresholds predetermined. This can lea
Externí odkaz:
http://arxiv.org/abs/2401.10700
In this paper, we introduce Recon3DMind, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant advancement in the fields of cognitive neuroscience and computer vision. T
Externí odkaz:
http://arxiv.org/abs/2312.07485
Autor:
Guo, Jianxiong
In this survey, we offer an extensive overview of the Online Influence Maximization (IM) problem by covering both theoretical aspects and practical applications. For the integrity of the article and because the online algorithm takes an offline oracl
Externí odkaz:
http://arxiv.org/abs/2312.00099